Overview

Dataset statistics

Number of variables18
Number of observations4585
Missing cells18100
Missing cells (%)21.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory613.6 KiB
Average record size in memory137.0 B

Variable types

Numeric11
Text2
Categorical4
Boolean1

Alerts

publish has constant value ""Constant
id is highly overall correlated with yearHigh correlation
year is highly overall correlated with idHigh correlation
damageAmountOrder is highly overall correlated with housesDestroyedAmountOrderTotalHigh correlation
housesDestroyedAmountOrderTotal is highly overall correlated with damageAmountOrderHigh correlation
month has 81 (1.8%) missing valuesMissing
day has 128 (2.8%) missing valuesMissing
hour has 747 (16.3%) missing valuesMissing
eqDepth has 1254 (27.4%) missing valuesMissing
damageAmountOrder has 1094 (23.9%) missing valuesMissing
damageMillionsDollars has 4029 (87.9%) missing valuesMissing
housesDestroyedAmountOrderTotal has 2900 (63.2%) missing valuesMissing
deathsAmountOrder has 2487 (54.2%) missing valuesMissing
injuriesAmountOrderTotal has 2992 (65.3%) missing valuesMissing
intensity has 2382 (52.0%) missing valuesMissing
id has unique valuesUnique
hour has 185 (4.0%) zerosZeros

Reproduction

Analysis started2023-06-30 04:48:20.344227
Analysis finished2023-06-30 04:49:13.359591
Duration53.02 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4585
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5652.9954
Minimum38
Maximum10699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:13.682928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile1063.6
Q13539
median5351
Q37926
95-th percentile10429.8
Maximum10699
Range10661
Interquartile range (IQR)4387

Descriptive statistics

Standard deviation2869.6408
Coefficient of variation (CV)0.50763189
Kurtosis-0.86208227
Mean5652.9954
Median Absolute Deviation (MAD)2104
Skewness0.18792961
Sum25918984
Variance8234838.2
MonotonicityNot monotonic
2023-06-30T01:49:14.123722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
942 1
 
< 0.1%
4714 1
 
< 0.1%
4046 1
 
< 0.1%
2987 1
 
< 0.1%
10094 1
 
< 0.1%
5698 1
 
< 0.1%
7911 1
 
< 0.1%
2120 1
 
< 0.1%
5285 1
 
< 0.1%
885 1
 
< 0.1%
Other values (4575) 4575
99.8%
ValueCountFrequency (%)
38 1
< 0.1%
43 1
< 0.1%
60 1
< 0.1%
62 1
< 0.1%
64 1
< 0.1%
65 1
< 0.1%
66 1
< 0.1%
70 1
< 0.1%
71 1
< 0.1%
74 1
< 0.1%
ValueCountFrequency (%)
10699 1
< 0.1%
10698 1
< 0.1%
10697 1
< 0.1%
10696 1
< 0.1%
10695 1
< 0.1%
10694 1
< 0.1%
10693 1
< 0.1%
10692 1
< 0.1%
10691 1
< 0.1%
10689 1
< 0.1%

year
Real number (ℝ)

Distinct579
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1900.2595
Minimum10
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:14.534160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1568.4
Q11907
median1968
Q32003
95-th percentile2018
Maximum2023
Range2013
Interquartile range (IQR)96

Descriptive statistics

Standard deviation230.08548
Coefficient of variation (CV)0.12108108
Kurtosis25.2693
Mean1900.2595
Median Absolute Deviation (MAD)40
Skewness-4.5949373
Sum8712690
Variance52939.327
MonotonicityNot monotonic
2023-06-30T01:49:14.987555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2004 78
 
1.7%
2008 76
 
1.7%
2003 72
 
1.6%
2018 69
 
1.5%
2007 67
 
1.5%
2017 64
 
1.4%
2006 63
 
1.4%
2010 62
 
1.4%
2019 61
 
1.3%
2009 61
 
1.3%
Other values (569) 3912
85.3%
ValueCountFrequency (%)
10 1
< 0.1%
27 1
< 0.1%
46 1
< 0.1%
62 1
< 0.1%
89 1
< 0.1%
100 1
< 0.1%
103 1
< 0.1%
115 1
< 0.1%
120 1
< 0.1%
123 1
< 0.1%
ValueCountFrequency (%)
2023 24
 
0.5%
2022 42
0.9%
2021 42
0.9%
2020 28
0.6%
2019 61
1.3%
2018 69
1.5%
2017 64
1.4%
2016 53
1.2%
2015 48
1.0%
2014 56
1.2%

month
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing81
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean6.4897869
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:15.346813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4449295
Coefficient of variation (CV)0.53082322
Kurtosis-1.2093031
Mean6.4897869
Median Absolute Deviation (MAD)3
Skewness-0.0067013412
Sum29230
Variance11.867539
MonotonicityNot monotonic
2023-06-30T01:49:15.645420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 393
8.6%
8 392
8.5%
7 391
8.5%
11 388
8.5%
1 384
8.4%
5 380
8.3%
3 376
8.2%
2 370
8.1%
4 368
8.0%
12 362
7.9%
Other values (2) 700
15.3%
ValueCountFrequency (%)
1 384
8.4%
2 370
8.1%
3 376
8.2%
4 368
8.0%
5 380
8.3%
6 354
7.7%
7 391
8.5%
8 392
8.5%
9 393
8.6%
10 346
7.5%
ValueCountFrequency (%)
12 362
7.9%
11 388
8.5%
10 346
7.5%
9 393
8.6%
8 392
8.5%
7 391
8.5%
6 354
7.7%
5 380
8.3%
4 368
8.0%
3 376
8.2%

day
Real number (ℝ)

Distinct31
Distinct (%)0.7%
Missing128
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean15.752973
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:16.007015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.750494
Coefficient of variation (CV)0.55548207
Kurtosis-1.1723279
Mean15.752973
Median Absolute Deviation (MAD)8
Skewness-0.01971964
Sum70211
Variance76.571145
MonotonicityNot monotonic
2023-06-30T01:49:16.350657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
25 183
 
4.0%
18 175
 
3.8%
1 172
 
3.8%
14 166
 
3.6%
20 165
 
3.6%
24 158
 
3.4%
9 157
 
3.4%
22 152
 
3.3%
16 151
 
3.3%
26 151
 
3.3%
Other values (21) 2827
61.7%
ValueCountFrequency (%)
1 172
3.8%
2 133
2.9%
3 141
3.1%
4 138
3.0%
5 137
3.0%
6 140
3.1%
7 140
3.1%
8 145
3.2%
9 157
3.4%
10 149
3.2%
ValueCountFrequency (%)
31 91
2.0%
30 123
2.7%
29 131
2.9%
28 136
3.0%
27 112
2.4%
26 151
3.3%
25 183
4.0%
24 158
3.4%
23 146
3.2%
22 152
3.3%

hour
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)0.6%
Missing747
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean11.318916
Minimum0
Maximum23
Zeros185
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:16.712276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.0384411
Coefficient of variation (CV)0.62182995
Kurtosis-1.2293555
Mean11.318916
Median Absolute Deviation (MAD)6
Skewness0.031659774
Sum43442
Variance49.539653
MonotonicityNot monotonic
2023-06-30T01:49:17.056979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 205
 
4.5%
0 185
 
4.0%
10 173
 
3.8%
21 171
 
3.7%
19 171
 
3.7%
8 166
 
3.6%
7 163
 
3.6%
23 163
 
3.6%
1 163
 
3.6%
14 162
 
3.5%
Other values (14) 2116
46.2%
(Missing) 747
 
16.3%
ValueCountFrequency (%)
0 185
4.0%
1 163
3.6%
2 205
4.5%
3 151
3.3%
4 151
3.3%
5 152
3.3%
6 152
3.3%
7 163
3.6%
8 166
3.6%
9 160
3.5%
ValueCountFrequency (%)
23 163
3.6%
22 160
3.5%
21 171
3.7%
20 152
3.3%
19 171
3.7%
18 146
3.2%
17 161
3.5%
16 137
3.0%
15 134
2.9%
14 162
3.5%
Distinct3011
Distinct (%)65.7%
Missing1
Missing (%)< 0.1%
Memory size35.9 KiB
2023-06-30T01:49:17.652802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length52
Median length40
Mean length25.265271
Min length4

Characters and Unicode

Total characters115816
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2506 ?
Unique (%)54.7%

Sample

1st rowJAPAN: UCHIURA BAY
2nd rowJAPAN: NEAR S COAST HONSHU: KOZU-SHIMA
3rd rowRWANDA: RUBAVU; CONGO
4th rowCHINA: SHAANXI PROVINCE
5th rowPERU: CAMANA, AREQUIPA
ValueCountFrequency (%)
china 587
 
4.1%
province 527
 
3.7%
islands 398
 
2.8%
japan 349
 
2.4%
indonesia 319
 
2.2%
iran 260
 
1.8%
new 238
 
1.6%
turkey 220
 
1.5%
island 179
 
1.2%
yunnan 155
 
1.1%
Other values (3531) 11196
77.6%
2023-06-30T01:49:18.782021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 15181
13.1%
14485
12.5%
N 9751
 
8.4%
I 9118
 
7.9%
E 6404
 
5.5%
S 5503
 
4.8%
R 5252
 
4.5%
O 5205
 
4.5%
: 4957
 
4.3%
L 4001
 
3.5%
Other values (32) 35959
31.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93774
81.0%
Space Separator 14485
 
12.5%
Other Punctuation 6981
 
6.0%
Dash Punctuation 288
 
0.2%
Close Punctuation 142
 
0.1%
Open Punctuation 142
 
0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15181
16.2%
N 9751
 
10.4%
I 9118
 
9.7%
E 6404
 
6.8%
S 5503
 
5.9%
R 5252
 
5.6%
O 5205
 
5.6%
L 4001
 
4.3%
U 3985
 
4.2%
C 3833
 
4.1%
Other values (16) 25541
27.2%
Other Punctuation
ValueCountFrequency (%)
: 4957
71.0%
, 1722
 
24.7%
; 184
 
2.6%
. 88
 
1.3%
' 28
 
0.4%
& 1
 
< 0.1%
\ 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
o 2
50.0%
f 1
25.0%
i 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 140
98.6%
] 2
 
1.4%
Open Punctuation
ValueCountFrequency (%)
( 140
98.6%
[ 2
 
1.4%
Space Separator
ValueCountFrequency (%)
14485
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93778
81.0%
Common 22038
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15181
16.2%
N 9751
 
10.4%
I 9118
 
9.7%
E 6404
 
6.8%
S 5503
 
5.9%
R 5252
 
5.6%
O 5205
 
5.6%
L 4001
 
4.3%
U 3985
 
4.2%
C 3833
 
4.1%
Other values (19) 25545
27.2%
Common
ValueCountFrequency (%)
14485
65.7%
: 4957
 
22.5%
, 1722
 
7.8%
- 288
 
1.3%
; 184
 
0.8%
) 140
 
0.6%
( 140
 
0.6%
. 88
 
0.4%
' 28
 
0.1%
[ 2
 
< 0.1%
Other values (3) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15181
13.1%
14485
12.5%
N 9751
 
8.4%
I 9118
 
7.9%
E 6404
 
5.5%
S 5503
 
4.8%
R 5252
 
4.5%
O 5205
 
4.5%
: 4957
 
4.3%
L 4001
 
3.5%
Other values (32) 35959
31.0%

latitude
Real number (ℝ)

Distinct3060
Distinct (%)66.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.361714
Minimum-62.877
Maximum73.122
Zeros4
Zeros (%)0.1%
Negative1066
Negative (%)23.2%
Memory size35.9 KiB
2023-06-30T01:49:19.237018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-62.877
5-th percentile-27
Q12.5
median29.2
Q338.2955
95-th percentile46.3372
Maximum73.122
Range135.999
Interquartile range (IQR)35.7955

Descriptive statistics

Standard deviation23.914591
Coefficient of variation (CV)1.1744881
Kurtosis0.029625077
Mean20.361714
Median Absolute Deviation (MAD)11.788
Skewness-0.89971185
Sum93317.737
Variance571.90765
MonotonicityNot monotonic
2023-06-30T01:49:19.660237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 26
 
0.6%
40.5 25
 
0.5%
35.5 18
 
0.4%
36.5 17
 
0.4%
33 16
 
0.3%
40 16
 
0.3%
38.5 16
 
0.3%
38 16
 
0.3%
37.5 15
 
0.3%
38.1 15
 
0.3%
Other values (3050) 4403
96.0%
ValueCountFrequency (%)
-62.877 1
< 0.1%
-61.825 1
< 0.1%
-60.82 1
< 0.1%
-60.532 1
< 0.1%
-60.274 1
< 0.1%
-59.5 1
< 0.1%
-59.1 1
< 0.1%
-58.893 1
< 0.1%
-58.416 1
< 0.1%
-58 1
< 0.1%
ValueCountFrequency (%)
73.122 1
< 0.1%
66.416 1
< 0.1%
66.16 1
< 0.1%
65.17 1
< 0.1%
64.66 1
< 0.1%
64.004 1
< 0.1%
64 1
< 0.1%
63.98 1
< 0.1%
63.966 1
< 0.1%
63.9 1
< 0.1%

longitude
Real number (ℝ)

Distinct3541
Distinct (%)77.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean46.044822
Minimum-179.984
Maximum180
Zeros0
Zeros (%)0.0%
Negative1157
Negative (%)25.2%
Memory size35.9 KiB
2023-06-30T01:49:20.097548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-179.984
5-th percentile-116.4242
Q1-0.9425
median67.9
Q3121.165
95-th percentile159.2135
Maximum180
Range359.984
Interquartile range (IQR)122.1075

Descriptive statistics

Standard deviation90.923376
Coefficient of variation (CV)1.974671
Kurtosis-0.69385977
Mean46.044822
Median Absolute Deviation (MAD)55.18
Skewness-0.64785605
Sum211023.42
Variance8267.0603
MonotonicityNot monotonic
2023-06-30T01:49:20.474412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-71 18
 
0.4%
-72 13
 
0.3%
102.5 12
 
0.3%
126.5 11
 
0.2%
126 11
 
0.2%
-79 10
 
0.2%
35.5 10
 
0.2%
120.5 10
 
0.2%
122 10
 
0.2%
-71.5 10
 
0.2%
Other values (3531) 4468
97.4%
ValueCountFrequency (%)
-179.984 1
< 0.1%
-179.971 1
< 0.1%
-179.513 1
< 0.1%
-179 2
< 0.1%
-178.55 1
< 0.1%
-178.5 1
< 0.1%
-178.413 1
< 0.1%
-178.4 1
< 0.1%
-178.252 1
< 0.1%
-178.153 1
< 0.1%
ValueCountFrequency (%)
180 1
< 0.1%
179.6 1
< 0.1%
179.444 1
< 0.1%
179.35 1
< 0.1%
179.2 1
< 0.1%
179.146 1
< 0.1%
179.1 1
< 0.1%
178.88 1
< 0.1%
178.87 1
< 0.1%
178.85 1
< 0.1%

eqMagnitude
Real number (ℝ)

Distinct65
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4463904
Minimum1.6
Maximum9.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:20.898287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.7
Q15.7
median6.5
Q37.3
95-th percentile8
Maximum9.5
Range7.9
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.038084
Coefficient of variation (CV)0.16103338
Kurtosis-0.28147376
Mean6.4463904
Median Absolute Deviation (MAD)0.8
Skewness-0.28229489
Sum29556.7
Variance1.0776184
MonotonicityNot monotonic
2023-06-30T01:49:21.306754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.5 262
 
5.7%
7 238
 
5.2%
6 221
 
4.8%
6.5 204
 
4.4%
5.5 186
 
4.1%
6.8 160
 
3.5%
6.3 149
 
3.2%
7.6 148
 
3.2%
6.4 141
 
3.1%
7.3 139
 
3.0%
Other values (55) 2737
59.7%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
2.1 2
< 0.1%
2.2 1
 
< 0.1%
2.8 1
 
< 0.1%
3.1 3
0.1%
3.2 4
0.1%
3.4 2
< 0.1%
3.5 4
0.1%
3.6 3
0.1%
3.7 3
0.1%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.2 1
 
< 0.1%
9.1 3
 
0.1%
9 2
 
< 0.1%
8.8 3
 
0.1%
8.7 3
 
0.1%
8.6 12
 
0.3%
8.5 18
0.4%
8.4 22
0.5%
8.3 39
0.9%

eqDepth
Real number (ℝ)

Distinct203
Distinct (%)6.1%
Missing1254
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean40.139898
Minimum0
Maximum675
Zeros12
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:21.712871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median24
Q337
95-th percentile120
Maximum675
Range675
Interquartile range (IQR)27

Descriptive statistics

Standard deviation70.004236
Coefficient of variation (CV)1.7440063
Kurtosis42.988812
Mean40.139898
Median Absolute Deviation (MAD)14
Skewness5.9968315
Sum133706
Variance4900.593
MonotonicityNot monotonic
2023-06-30T01:49:22.136773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 507
 
11.1%
33 370
 
8.1%
15 153
 
3.3%
60 115
 
2.5%
20 100
 
2.2%
5 94
 
2.1%
25 81
 
1.8%
30 72
 
1.6%
12 67
 
1.5%
11 64
 
1.4%
Other values (193) 1708
37.3%
(Missing) 1254
27.4%
ValueCountFrequency (%)
0 12
 
0.3%
1 15
 
0.3%
2 9
 
0.2%
3 15
 
0.3%
4 16
 
0.3%
5 94
2.1%
6 38
0.8%
7 43
0.9%
8 59
1.3%
9 45
1.0%
ValueCountFrequency (%)
675 1
< 0.1%
670 1
< 0.1%
664 1
< 0.1%
651 1
< 0.1%
650 2
< 0.1%
640 1
< 0.1%
636 1
< 0.1%
633 1
< 0.1%
631 1
< 0.1%
629 1
< 0.1%

damageAmountOrder
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.1%
Missing1094
Missing (%)23.9%
Memory size35.9 KiB
2.0
1195 
1.0
1027 
3.0
800 
4.0
469 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10473
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 1195
26.1%
1.0 1027
22.4%
3.0 800
17.4%
4.0 469
 
10.2%
(Missing) 1094
23.9%

Length

2023-06-30T01:49:22.512917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-30T01:49:22.905694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1195
34.2%
1.0 1027
29.4%
3.0 800
22.9%
4.0 469
 
13.4%

Most occurring characters

ValueCountFrequency (%)
. 3491
33.3%
0 3491
33.3%
2 1195
 
11.4%
1 1027
 
9.8%
3 800
 
7.6%
4 469
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6982
66.7%
Other Punctuation 3491
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3491
50.0%
2 1195
 
17.1%
1 1027
 
14.7%
3 800
 
11.5%
4 469
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 3491
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10473
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3491
33.3%
0 3491
33.3%
2 1195
 
11.4%
1 1027
 
9.8%
3 800
 
7.6%
4 469
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3491
33.3%
0 3491
33.3%
2 1195
 
11.4%
1 1027
 
9.8%
3 800
 
7.6%
4 469
 
4.5%

damageMillionsDollars
Real number (ℝ)

Distinct277
Distinct (%)49.8%
Missing4029
Missing (%)87.9%
Infinite0
Infinite (%)0.0%
Mean1364.7314
Minimum0.013
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:23.323359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.013
5-th percentile0.675
Q14.305
median25
Q3232.475
95-th percentile5225
Maximum100000
Range99999.987
Interquartile range (IQR)228.17

Descriptive statistics

Standard deviation7434.8239
Coefficient of variation (CV)5.4478294
Kurtosis119.16249
Mean1364.7314
Median Absolute Deviation (MAD)24
Skewness10.291632
Sum758790.66
Variance55276606
MonotonicityNot monotonic
2023-06-30T01:49:24.034921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 49
 
1.1%
1 31
 
0.7%
2 15
 
0.3%
25 12
 
0.3%
100 12
 
0.3%
20 11
 
0.2%
50 9
 
0.2%
10 9
 
0.2%
0.5 9
 
0.2%
1000 8
 
0.2%
Other values (267) 391
 
8.5%
(Missing) 4029
87.9%
ValueCountFrequency (%)
0.013 1
< 0.1%
0.025 1
< 0.1%
0.04 1
< 0.1%
0.05 1
< 0.1%
0.1 2
< 0.1%
0.15 1
< 0.1%
0.2 2
< 0.1%
0.25 1
< 0.1%
0.3 1
< 0.1%
0.32 1
< 0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
87500 1
 
< 0.1%
86000 1
 
< 0.1%
40000 1
 
< 0.1%
30000 1
 
< 0.1%
28000 1
 
< 0.1%
20000 3
0.1%
16200 1
 
< 0.1%
15800 1
 
< 0.1%
15000 1
 
< 0.1%

publish
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
True
4585 
ValueCountFrequency (%)
True 4585
100.0%
2023-06-30T01:49:24.362939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

housesDestroyedAmountOrderTotal
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.2%
Missing2900
Missing (%)63.2%
Memory size35.9 KiB
3.0
622 
4.0
443 
1.0
359 
2.0
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5055
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row1.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 622
 
13.6%
4.0 443
 
9.7%
1.0 359
 
7.8%
2.0 261
 
5.7%
(Missing) 2900
63.2%

Length

2023-06-30T01:49:24.614773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-30T01:49:25.023720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 622
36.9%
4.0 443
26.3%
1.0 359
21.3%
2.0 261
15.5%

Most occurring characters

ValueCountFrequency (%)
. 1685
33.3%
0 1685
33.3%
3 622
 
12.3%
4 443
 
8.8%
1 359
 
7.1%
2 261
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3370
66.7%
Other Punctuation 1685
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1685
50.0%
3 622
 
18.5%
4 443
 
13.1%
1 359
 
10.7%
2 261
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 1685
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5055
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1685
33.3%
0 1685
33.3%
3 622
 
12.3%
4 443
 
8.8%
1 359
 
7.1%
2 261
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1685
33.3%
0 1685
33.3%
3 622
 
12.3%
4 443
 
8.8%
1 359
 
7.1%
2 261
 
5.2%
Distinct146
Distinct (%)3.2%
Missing1
Missing (%)< 0.1%
Memory size35.9 KiB
2023-06-30T01:49:25.477307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length30
Mean length7.0543194
Min length2

Characters and Unicode

Total characters32337
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.5%

Sample

1st rowJAPAN
2nd rowJAPAN
3rd rowRWANDA
4th rowCHINA
5th rowPERU
ValueCountFrequency (%)
china 584
 
11.2%
japan 349
 
6.7%
indonesia 329
 
6.3%
iran 258
 
4.9%
usa 245
 
4.7%
turkey 214
 
4.1%
new 203
 
3.9%
peru 153
 
2.9%
greece 150
 
2.9%
chile 146
 
2.8%
Other values (155) 2601
49.7%
2023-06-30T01:49:26.401198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 5357
16.6%
I 3750
11.6%
N 3587
11.1%
E 2634
 
8.1%
S 1669
 
5.2%
R 1612
 
5.0%
U 1422
 
4.4%
C 1414
 
4.4%
O 1291
 
4.0%
P 1247
 
3.9%
Other values (23) 8354
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 31558
97.6%
Space Separator 648
 
2.0%
Open Punctuation 57
 
0.2%
Close Punctuation 57
 
0.2%
Dash Punctuation 9
 
< 0.1%
Other Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5357
17.0%
I 3750
11.9%
N 3587
11.4%
E 2634
 
8.3%
S 1669
 
5.3%
R 1612
 
5.1%
U 1422
 
4.5%
C 1414
 
4.5%
O 1291
 
4.1%
P 1247
 
4.0%
Other values (16) 7575
24.0%
Other Punctuation
ValueCountFrequency (%)
, 3
37.5%
. 3
37.5%
' 2
25.0%
Space Separator
ValueCountFrequency (%)
648
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31558
97.6%
Common 779
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5357
17.0%
I 3750
11.9%
N 3587
11.4%
E 2634
 
8.3%
S 1669
 
5.3%
R 1612
 
5.1%
U 1422
 
4.5%
C 1414
 
4.5%
O 1291
 
4.1%
P 1247
 
4.0%
Other values (16) 7575
24.0%
Common
ValueCountFrequency (%)
648
83.2%
( 57
 
7.3%
) 57
 
7.3%
- 9
 
1.2%
, 3
 
0.4%
. 3
 
0.4%
' 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 5357
16.6%
I 3750
11.6%
N 3587
11.1%
E 2634
 
8.1%
S 1669
 
5.2%
R 1612
 
5.0%
U 1422
 
4.4%
C 1414
 
4.4%
O 1291
 
4.0%
P 1247
 
3.9%
Other values (23) 8354
25.8%
Distinct5
Distinct (%)0.2%
Missing2487
Missing (%)54.2%
Memory size35.9 KiB
1.0
1255 
3.0
397 
4.0
254 
2.0
191 
0.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6294
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 1255
27.4%
3.0 397
 
8.7%
4.0 254
 
5.5%
2.0 191
 
4.2%
0.0 1
 
< 0.1%
(Missing) 2487
54.2%

Length

2023-06-30T01:49:26.762556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-30T01:49:27.170979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1255
59.8%
3.0 397
 
18.9%
4.0 254
 
12.1%
2.0 191
 
9.1%
0.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2099
33.3%
. 2098
33.3%
1 1255
19.9%
3 397
 
6.3%
4 254
 
4.0%
2 191
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4196
66.7%
Other Punctuation 2098
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2099
50.0%
1 1255
29.9%
3 397
 
9.5%
4 254
 
6.1%
2 191
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 2098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6294
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2099
33.3%
. 2098
33.3%
1 1255
19.9%
3 397
 
6.3%
4 254
 
4.0%
2 191
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2099
33.3%
. 2098
33.3%
1 1255
19.9%
3 397
 
6.3%
4 254
 
4.0%
2 191
 
3.0%
Distinct4
Distinct (%)0.3%
Missing2992
Missing (%)65.3%
Memory size35.9 KiB
1.0
807 
3.0
406 
2.0
221 
4.0
159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4779
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 807
 
17.6%
3.0 406
 
8.9%
2.0 221
 
4.8%
4.0 159
 
3.5%
(Missing) 2992
65.3%

Length

2023-06-30T01:49:27.514618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-30T01:49:27.906464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 807
50.7%
3.0 406
25.5%
2.0 221
 
13.9%
4.0 159
 
10.0%

Most occurring characters

ValueCountFrequency (%)
. 1593
33.3%
0 1593
33.3%
1 807
16.9%
3 406
 
8.5%
2 221
 
4.6%
4 159
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3186
66.7%
Other Punctuation 1593
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1593
50.0%
1 807
25.3%
3 406
 
12.7%
2 221
 
6.9%
4 159
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 1593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4779
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1593
33.3%
0 1593
33.3%
1 807
16.9%
3 406
 
8.5%
2 221
 
4.6%
4 159
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1593
33.3%
0 1593
33.3%
1 807
16.9%
3 406
 
8.5%
2 221
 
4.6%
4 159
 
3.3%

intensity
Real number (ℝ)

Distinct11
Distinct (%)0.5%
Missing2382
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean7.8565592
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.9 KiB
2023-06-30T01:49:28.236611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median8
Q39
95-th percentile10
Maximum12
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7197724
Coefficient of variation (CV)0.21889638
Kurtosis0.19098803
Mean7.8565592
Median Absolute Deviation (MAD)1
Skewness-0.40883737
Sum17308
Variance2.957617
MonotonicityNot monotonic
2023-06-30T01:49:28.533390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 553
 
12.1%
7 445
 
9.7%
9 393
 
8.6%
10 294
 
6.4%
6 220
 
4.8%
5 114
 
2.5%
11 91
 
2.0%
4 58
 
1.3%
3 19
 
0.4%
2 8
 
0.2%
(Missing) 2382
52.0%
ValueCountFrequency (%)
2 8
 
0.2%
3 19
 
0.4%
4 58
 
1.3%
5 114
 
2.5%
6 220
 
4.8%
7 445
9.7%
8 553
12.1%
9 393
8.6%
10 294
6.4%
11 91
 
2.0%
ValueCountFrequency (%)
12 8
 
0.2%
11 91
 
2.0%
10 294
6.4%
9 393
8.6%
8 553
12.1%
7 445
9.7%
6 220
 
4.8%
5 114
 
2.5%
4 58
 
1.3%
3 19
 
0.4%

Interactions

2023-06-30T01:49:06.193583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:23.528041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:27.760010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:32.287389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:36.414751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:40.581769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:44.701117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:49.062040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:53.536948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:57.831328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:02.175189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:06.567633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:23.918355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:28.152775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:32.667459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:36.784589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:40.938831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:45.130310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:49.418787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:53.939702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:58.210875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:02.510349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:06.990192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:24.309618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:28.564891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:33.069284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:37.189104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:41.285648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:45.593065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:49.846581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:54.340605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:58.610502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:02.916152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:07.344524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:24.687473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:28.957417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:33.410737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:37.548697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:41.673961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:46.000394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:50.209506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:54.703899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:59.014988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:03.266049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:07.707501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:25.067661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:29.331608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:33.810004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:37.944734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:42.040760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:46.374780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:50.575195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:55.119506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:59.384195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:03.622210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:08.086915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:25.403636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:29.703656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:34.155993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:38.285061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:42.371836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:46.758965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:51.218749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:55.477316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:59.757731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:04.004577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:08.444026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:25.813756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:30.275558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:34.531240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:38.679321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:42.770067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:47.125465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:51.607571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:55.883968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:00.215857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:04.351783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:08.821044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:26.200400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:30.703058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:34.919927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:39.061795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:43.175674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:47.499762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:52.001868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:56.254861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:00.590883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:04.717762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:09.210839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:26.612669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:31.110958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:35.293809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:39.445590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:43.535813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:47.907764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:52.388879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:56.653448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:01.019778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:05.122825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:09.570215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:27.022920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:31.532307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:35.691252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:39.843704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:43.953577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:48.290148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:52.796488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:57.085562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:01.415628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:05.472465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:09.974186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:27.381000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:31.908023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:36.045307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:40.200924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:44.317811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:48.643154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:53.189666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:48:57.451651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:01.793077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-30T01:49:05.846491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-30T01:49:28.848083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idyearmonthdayhourlatitudelongitudeeqMagnitudeeqDepthdamageMillionsDollarsintensitydamageAmountOrderhousesDestroyedAmountOrderTotaldeathsAmountOrderinjuriesAmountOrderTotal
id1.0000.7040.007-0.0080.026-0.1100.076-0.324-0.2530.316-0.4860.2450.2010.2470.163
year0.7041.000-0.014-0.0050.011-0.128-0.017-0.322-0.2990.352-0.4270.1130.0720.1990.039
month0.007-0.0141.0000.0220.003-0.0260.0340.029-0.013-0.010-0.0210.0000.0000.0000.000
day-0.008-0.0050.0221.0000.0140.016-0.0150.011-0.0020.014-0.0030.0000.0520.0270.027
hour0.0260.0110.0030.0141.000-0.012-0.0110.0100.0100.013-0.0630.0000.0170.0360.000
latitude-0.110-0.128-0.0260.016-0.0121.000-0.030-0.212-0.2370.1450.1350.1060.1280.0880.020
longitude0.076-0.0170.034-0.015-0.011-0.0301.0000.1060.1010.051-0.1030.1130.1230.0810.066
eqMagnitude-0.324-0.3220.0290.0110.010-0.2120.1061.0000.4180.1950.4260.1670.1420.1600.197
eqDepth-0.253-0.299-0.013-0.0020.010-0.2370.1010.4181.000-0.1500.0020.0290.0640.0000.000
damageMillionsDollars0.3160.352-0.0100.0140.0130.1450.0510.195-0.1501.0000.2940.0000.0000.1220.109
intensity-0.486-0.427-0.021-0.003-0.0630.135-0.1030.4260.0020.2941.0000.2850.2670.2710.233
damageAmountOrder0.2450.1130.0000.0000.0000.1060.1130.1670.0290.0000.2851.0000.5010.3190.360
housesDestroyedAmountOrderTotal0.2010.0720.0000.0520.0170.1280.1230.1420.0640.0000.2670.5011.0000.2810.329
deathsAmountOrder0.2470.1990.0000.0270.0360.0880.0810.1600.0000.1220.2710.3190.2811.0000.439
injuriesAmountOrderTotal0.1630.0390.0000.0270.0000.0200.0660.1970.0000.1090.2330.3600.3290.4391.000

Missing values

2023-06-30T01:49:10.517395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-30T01:49:11.882275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-30T01:49:12.737190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idyearmonthdayhourlocationNamelatitudelongitudeeqMagnitudeeqDepthdamageAmountOrderdamageMillionsDollarspublishhousesDestroyedAmountOrderTotalcountrydeathsAmountOrderinjuriesAmountOrderTotalintensity
094216407.031.0NaNJAPAN: UCHIURA BAY42.070140.6806.5NaN1.0NaNTrue1.0JAPANNaNNaNNaN
1556820007.01.07.0JAPAN: NEAR S COAST HONSHU: KOZU-SHIMA34.221139.1316.110.01.0NaNTrueNaNJAPAN1.03.0NaN
21054520215.025.09.0RWANDA: RUBAVU; CONGO-1.60129.4004.710.02.0NaNTrue4.0RWANDANaNNaN7.0
3805119598.010.023.0CHINA: SHAANXI PROVINCE35.600110.9005.4NaN1.0NaNTrue1.0CHINANaNNaN7.0
4552019994.03.06.0PERU: CAMANA, AREQUIPA-16.660-72.6626.887.02.0NaNTrueNaNPERU1.01.06.0
5955220109.027.011.0IRAN: SHIRAZ29.64751.6655.827.01.0NaNTrue1.0IRAN1.01.0NaN
6286619095.011.015.0CHINA: YUNNAN PROVINCE24.400103.0006.5NaN2.0NaNTrue3.0CHINA1.0NaN8.0
7543319962.017.05.0INDONESIA: NEW GUINEA: IRIAN JAYA: BIAK, SUPIORI-0.891136.9528.233.02.04.2True4.0INDONESIA2.03.09.0
8813020085.07.016.0JAPAN: HONSHU: E COAST36.158141.5216.839.01.0NaNTrueNaNJAPANNaN1.0NaN
9241918925.016.011.0GUAM14.000143.3007.5NaN2.0NaNTrue1.0USA TERRITORYNaNNaN8.0
idyearmonthdayhourlocationNamelatitudelongitudeeqMagnitudeeqDepthdamageAmountOrderdamageMillionsDollarspublishhousesDestroyedAmountOrderTotalcountrydeathsAmountOrderinjuriesAmountOrderTotalintensity
4575571220038.04.04.0SCOTIA SEA: SOUTH ORKNEY ISLANDS: LAURIE IS-60.532-43.4117.610.01.0NaNTrueNaNANTARCTICANaNNaNNaN
45762819190710.016.014.0MEXICO: GULF OF CALIFORNIA28.000-112.5007.760.0NaNNaNTrueNaNMEXICONaNNaNNaN
4577118517132.026.0NaNCHINA: YUNNAN PROVINCE: S OF XUNDIAN25.400103.2006.8NaN4.0NaNTrue4.0CHINA4.0NaN9.0
45784824197812.023.011.0TAIWAN: PINTUNG23.247122.0757.033.0NaNNaNTrueNaNTAIWAN1.01.0NaN
4579369719424.08.015.0PHILIPPINES: MINDORO13.500121.0007.825.0NaNNaNTrueNaNPHILIPPINESNaNNaNNaN
45806261471NaNNaNNaNPERU-16.300-71.0008.025.0NaNNaNTrueNaNPERUNaNNaN9.0
45816408196312.016.01.0INDONESIA: JAVA: LABUHAN, MENES, PONOROGO-6.400105.4006.664.01.0NaNTrueNaNINDONESIANaNNaNNaN
45828161200810.06.08.0CHINA: TIBET (XIZANG PROVINCE)29.80790.3506.312.02.0NaNTrue3.0CHINA1.01.0NaN
458377715828.015.0NaNPERU-12.200-77.6007.830.0NaNNaNTrueNaNPERUNaNNaN7.0
45842838190810.023.020.0AFGHANISTAN: HINDU KUSH36.50070.5007.0220.0NaNNaNTrueNaNAFGHANISTANNaNNaNNaN